Short-Term Stock Price Prediction Based on Single and Stacking Machine Learning Models

" Objective - As the investment environment improves, individuals are increasingly eager to invest their idle funds. Securities companies have become the preferred choice for buying financial products. The current accuracy of stock predictions relies on the comprehensive models used by each securities company, including stock market trading, data, and stock pricing models. However, securities companies have not adequately explored a single suitable model for stock predictions and have rarely assessed the effectiveness of stacking and ensemble methods in improving these predictions. Methodology - This research first explored and proposed the best single-stock prediction model. Next, it combined four individual prediction models to create a stacking model. Findings - The comparison between the single and stacking models demonstrated that the stacking model's prediction accuracy exceeded that of the single model. Therefore, it is recommended that securities companies adopt a stacking-type prediction model to forecast share prices for their investment customers. Novelty - Using a stacking model could improve the accuracy of stock price predictions for investment managers, help users make better decisions, and ultimately enhance the company's earnings by delivering more accurate investment outcomes. Type of Paper - Empirical"